SOTAVerified

Click-Through Rate Prediction

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )

Papers

Showing 351–375 of 391 papers

TitleStatusHype
Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction—0
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding—0
Extreme Learning Machines for Fast Training of Click-Through Rate Prediction Models—0
Farthest Greedy Path Sampling for Two-shot Recommender Search—0
Feature Interaction based Neural Network for Click-Through Rate Prediction—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives—0
Feature Shapley: A general framework to discovering useful feature interactions—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction—0
Field-aware Neural Factorization Machine for Click-Through Rate Prediction—0
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction—0
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction—0
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems—0
FM^2: Field-matrixed Factorization Machines for Recommender Systems—0
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction—0
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction—0
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction—0
GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks—0
General Information Metrics for Improving AI Model Training Efficiency—0
Generative Click-through Rate Prediction with Applications to Search Advertising—0
GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation—0
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction—0
Graph Cross-Correlated Network for Recommendation—0
Graph Intention Network for Click-through Rate Prediction in Sponsored Search—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1QNN-αAUC0.82—Unverified
2FCNAUC0.82—Unverified
3GDCNAUC0.82—Unverified
4MemoNetAUC0.82—Unverified
5TF4CTRAUC0.82—Unverified
6FinalMLP + MMBAttnAUC0.81—Unverified
7FinalMLPAUC0.81—Unverified
8CETNAUC0.81—Unverified
9DNN + MMBAttnAUC0.81—Unverified
10STECAUC0.81—Unverified
#ModelMetricClaimedVerifiedStatus
1OptInterAUC0.81—Unverified
2OptInter-MAUC0.81—Unverified
3CELSAUC0.8—Unverified
4FCNAUC0.8—Unverified
5CETNAUC0.8—Unverified
6OptFSAUC0.8—Unverified
7OptEmbedAUC0.79—Unverified
8Sparse Deep FwFMAUC0.79—Unverified
9FGCNN+IPNNAUC0.79—Unverified
10Fi-GNNAUC0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepFMAUC0.87—Unverified
2FNNAUC0.87—Unverified
3Wide & Deep (LR & DNN)AUC0.87—Unverified
4PNN*AUC0.87—Unverified
5IPNNAUC0.87—Unverified
6Wide & Deep (FM & DNN)AUC0.87—Unverified
7OPNNAUC0.87—Unverified
8DeepMCPAUC0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1xDeepFMAUC0.84—Unverified
2Wide & DeepAUC0.84—Unverified
3DeepFMAUC0.84—Unverified
4PNNAUC0.83—Unverified
5RippleNetAUC0.68—Unverified
6DKNAUC0.66—Unverified
7DNNAUC0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1OPNNAUC0.82—Unverified
2IPNNAUC0.79—Unverified
3FCNAUC0.79—Unverified
4OptInterAUC0.78—Unverified
5OptInter-MAUC0.78—Unverified
6PNN*AUC0.77—Unverified
7FNNAUC0.76—Unverified
#ModelMetricClaimedVerifiedStatus
1FCNAUC0.86—Unverified
2DeepIMAUC0.85—Unverified
3xDeepFMAUC0.85—Unverified
4AutoInt+AUC0.85—Unverified
5DCNv2AUC0.85—Unverified
6DeepFMAUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1STECAUC0.97—Unverified
2KNIAUC0.94—Unverified
3RippleNetAUC0.92—Unverified
4MKRAUC0.92—Unverified
5DCNv3AUC0.91—Unverified
6AutoIntAUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1github.com/guotong1988/movielens_datasetAUC0.79—Unverified
2DIN + Dice ActivationAUC0.73—Unverified
3DINAUC0.73—Unverified
4DeepFMAUC0.73—Unverified
5PNNAUC0.73—Unverified
6Wide & DeepAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1DIN + Dice ActivationAUC0.89—Unverified
2DINAUC0.88—Unverified
3DeepFMAUC0.87—Unverified
4PNNAUC0.87—Unverified
5Wide & DeepAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1xDeepFMAUC0.86—Unverified
2DeepFMAUC0.85—Unverified
3PNNAUC0.84—Unverified
4Wide & DeepAUC0.84—Unverified
5DNNAUC0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1TF4CTRAUC0.99—Unverified
2FinalMLP + MMBAttnAUC0.99—Unverified
3FinalMLPAUC0.99—Unverified
4DNN + MMBAttnAUC0.99—Unverified
5AFN+AUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1FCNAUC0.81—Unverified
2MemoNetAUC0.81—Unverified
3OptEmbedAUC0.8—Unverified
4OptFSAUC0.8—Unverified
5AutoIntAUC0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1TF4CTRAUC0.97—Unverified
2FinalMLPAUC0.97—Unverified
3AFN+AUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSTN-IAUC0.84—Unverified
2DeepMCPAUC0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1KGCN-sumAUC0.74—Unverified
2RippleNetAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1KGCN-concatAUC0.8—Unverified
2MKRAUC0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1DIENAUC0.78—Unverified
#ModelMetricClaimedVerifiedStatus
1NormDNNAUC0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1MKRAUC0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1FGCNN+IPNNAUC0.94—Unverified